TY - GEN
T1 - Multi-source information gain for random forest
T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
AU - Alzheimer’s Disease Neuroimaging Initiative (ADNI)
AU - Huynh, Tri
AU - Gao, Yaozong
AU - Kang, Jiayin
AU - Wang, Li
AU - Zhang, Pei
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of multi-source information gain that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing multiple beneficial sources of information, instead of the sole governing of prediction targets as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the multi-source information gain concept effectively helps better guide the training process with consistent improvement in prediction accuracy.
AB - Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of multi-source information gain that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing multiple beneficial sources of information, instead of the sole governing of prediction targets as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the multi-source information gain concept effectively helps better guide the training process with consistent improvement in prediction accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84951985142&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_39
DO - 10.1007/978-3-319-24888-2_39
M3 - Conference contribution
AN - SCOPUS:84951985142
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 321
EP - 329
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
Y2 - 5 October 2015 through 5 October 2015
ER -